Abstract
We discuss how an autoencoder can detect system-level anomalies in a real-time gross settlement system by reconstructing a set of liquidity vectors. A liquidity vector is an aggregated representation of the underlying payment network of a settlement system for a particular time interval. Furthermore, we evaluate the performance of two autoencoders on real-world payment data extracted from the TARGET2 settlement system. We do this by generating different types of artificial bank runs in the data and determining how the autoencoders respond. Our experimental results show that the autoencoders are able to detect unexpected changes in the liquidity flows between banks.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) |
| Editors | Olivier Camp, Joaquim Filipe |
| Publisher | SciTePress |
| Pages | 433-441 |
| ISBN (Print) | 9789897282479 |
| DOIs | |
| Publication status | Published - 2017 |
| Event | 19th International Conference on Enterprise Information Systems - Porto, Portugal Duration: 26 Apr 2017 → 29 Apr 2017 |
Conference
| Conference | 19th International Conference on Enterprise Information Systems |
|---|---|
| Abbreviated title | ICEIS 2017 |
| Country/Territory | Portugal |
| City | Porto |
| Period | 26/04/17 → 29/04/17 |
Keywords
- anomaly detection
- neural network
- autoencoder
- real-time gross settlement system
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Dive into the research topics of 'Anomaly detection in real-time gross payment data'. Together they form a unique fingerprint.Prizes
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ICEIS 2017 Best Paper Award - Area: Artificial Intelligence and Decision Support Systems
Triepels, R. (Recipient), Daniels, H. (Recipient) & Heijmans, R. (Recipient), 2017
Prize